An Environmental Data Collection for COVID-19 Pandemic Research

Qian Liu, Wei Liu, Dexuan Sha, Shubham Kumar, Emily Chang, Vishakh Arora, Hai Lan, Yun Li, Zifu Wang, Yadong Zhang, Zhiran Zhang, Jackson T. Harris, Srikar Chinala and Chaowei Yang
Additional contact information
Qian Liu: NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
Wei Liu: NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
Dexuan Sha: NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
Shubham Kumar: Dougherty Valley High School, San Ramon, CA 94582, USA
Emily Chang: Albemarle High School, Charlottesville, VA 22901, USA
Vishakh Arora: Dougherty Valley High School, San Ramon, CA 94582, USA
Hai Lan: NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
Yun Li: NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
Zifu Wang: NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
Yadong Zhang: School of Geographical Sciences, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China
Zhiran Zhang: NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
Jackson T. Harris: NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
Srikar Chinala: University Preparatory Academy, San Jose, CA 95125, USA
Chaowei Yang: NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA

Data, 2020, vol. 5, issue 3, 1-13

Abstract: The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National Science Foundation (NSF) spatiotemporal innovation center constructed a spatiotemporal platform with various task forces including international researchers and implementation strategies. Compared to similar platforms that only offer viral and health data, this platform views virus-related environmental data collection (EDC) an important component for the geospatial analysis of the pandemic. The EDC contains environmental factors either proven or with potential to influence the spread of COVID-19 and virulence or influence the impact of the pandemic on human health (e.g., temperature, humidity, precipitation, air quality index and pollutants, nighttime light (NTL)). In this platform/framework, environmental data are processed and organized across multiple spatiotemporal scales for a variety of applications (e.g., global mapping of daily temperature, humidity, precipitation, correlation of the pandemic to the mean values of climate and weather factors by city). This paper introduces the raw input data, construction and metadata of reprocessed data, and data storage, as well as the sharing and quality control methodologies of the COVID-19 related environmental data collection.

Keywords: COVID-19; decision support; rapid response; environmental data; spatiotemporal platform (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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